# DocHPLT: A Massively Multilingual Document-Level Translation Dataset

arXiv: https://arxiv.org/abs/2508.13079 · HTML: https://mlsanity.com/paper/2508.13079/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 52 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Builds on (most cited first, 29 of 29)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task](https://mlsanity.com/paper/2209.06243/) (2022)
- [CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB](https://mlsanity.com/paper/1911.04944/) (2019)
- [CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs](https://mlsanity.com/paper/1911.06154/) (2019)
- [Context-Aware Neural Machine Translation Learns Anaphora Resolution](https://mlsanity.com/paper/1805.10163/) (2018)
- [Neural Machine Translation with Extended Context](https://mlsanity.com/paper/1708.05943/) (2017)
- [Evaluating Discourse Phenomena in Neural Machine Translation](https://mlsanity.com/paper/1711.00513/) (2017)
- [WMT24++: Expanding the Language Coverage of WMT24 to 55 Languages & Dialects](https://mlsanity.com/paper/2502.12404/) (2025)
- [Improving the Transformer Translation Model with Document-Level Context](https://mlsanity.com/paper/1810.03581/) (2018)
- [Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca](https://mlsanity.com/paper/2309.08958/) (2023)
- [Adapting Large Language Models for Document-Level Machine Translation](https://mlsanity.com/paper/2401.06468/) (2024)
- [A New Massive Multilingual Dataset for High-Performance Language Technologies](https://mlsanity.com/paper/2403.14009/) (2024)
- [An Open Dataset and Model for Language Identification](https://mlsanity.com/paper/2305.13820/) (2023)
- [When and Why is Document-level Context Useful in Neural Machine Translation?](https://mlsanity.com/paper/1910.00294/) (2019)
- [Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature](https://mlsanity.com/paper/2210.14250/) (2022)
- [Document Context Neural Machine Translation with Memory Networks](https://mlsanity.com/paper/1711.03688/) (2017)
- [When Does Translation Require Context? A Data-driven, Multilingual Exploration](https://mlsanity.com/paper/2109.07446/) (2021)
- [Diving Deep into Context-Aware Neural Machine Translation](https://mlsanity.com/paper/2010.09482/) (2020)
- [An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT)](https://mlsanity.com/paper/2503.10267/) (2025)
- [Identifying Context-Dependent Translations for Evaluation Set Production](https://mlsanity.com/paper/2311.02321/) (2023)
- [Document-Level Language Models for Machine Translation](https://mlsanity.com/paper/2310.12303/) (2023)
- [AFRIDOC-MT: Document-level MT Corpus for African Languages](https://mlsanity.com/paper/2501.06374/) (2025)
- [Multilingual Contextualization of Large Language Models for Document-Level Machine Translation](https://mlsanity.com/paper/2504.12140/) (2025)
- [Investigating Length Issues in Document-level Machine Translation](https://mlsanity.com/paper/2412.17592/) (2024)
- [Source-primed Multi-turn Conversation Helps Large Language Models Translate Documents](https://mlsanity.com/paper/2503.10494/) (2025)
- [Can Your Context-Aware MT System Pass the DiP Benchmark Tests? : Evaluation Benchmarks for Discourse Phenomena in Machine Translation](https://mlsanity.com/paper/2004.14607/) (2020)
- [Recovering document annotations for sentence-level bitext](https://mlsanity.com/paper/2406.03869/) (2024)
- [A Bilingual Parallel Corpus with Discourse Annotations](https://mlsanity.com/paper/2210.14667/) (2022)
- [20min-XD: A Comparable Corpus of Swiss News Articles](https://mlsanity.com/paper/2504.21677/) (2025)

## Built on by (most cited first, 7 of 7)

- [HPLT 3.0: Very Large-Scale Multilingual Resources for LLMs and MT. Mono- and Bi-lingual Data, Multilingual Evaluation, and Pre-Trained Models](https://mlsanity.com/paper/2511.01066/) (2025)
- [What Does LLM Refinement Actually Improve? A Systematic Study on Document-Level Literary Translation](https://mlsanity.com/paper/2605.13368/) (2026)
- [Open Machine Translation for Esperanto](https://mlsanity.com/paper/2603.29345/) (2026)
- [ForMaT: Dataset for Visually-Grounded Multilingual PDF Translation](https://mlsanity.com/paper/2605.15794/) (2026)
- [CAT-Translate: Building Compact Open-Source Models for Japanese-English Translation](https://mlsanity.com/paper/2606.21413/) (2026)
- [Studying quantization trade-offs for efficient inference deployment in machine translation](https://mlsanity.com/paper/2607.29397/) (2026)
- [Doc2FRC: Length-Consistent Document-Level Machine Translation via Fixed-Range Chunking](https://mlsanity.com/paper/2609.12674/) (2026)

## Abstract (arXiv)

Existing document-level machine translation resources are only available for a handful of languages, mostly high-resourced ones. To facilitate the training and evaluation of document-level translation and, more broadly, long-context modeling for global communities, we create DocHPLT, the largest publicly available document-level translation dataset to date. It contains 124 million aligned document pairs across 50 languages paired with English, comprising 4.26 billion sentences. By adding pivoted alignments, practitioners can obtain 2500 additional pairs not involving English. Unlike previous reconstruction-based approaches that piece together documents from sentence-level data, we modify an existing web extraction pipeline to preserve complete document integrity from the source, retaining all content, including unaligned portions. After our preliminary experiments identify the optimal training context strategy for document-level translation, we demonstrate that LLMs fine-tuned on DocHPLT substantially outperform off-the-shelf instruction-tuned baselines, with particularly dramatic improvements for under-resourced languages. We open-source the dataset under a permissive license, providing essential infrastructure for advancing multilingual document-level translation.